Setup
Instalar version en desarrollo.
if (!require("remotes"))
install.packages("remotes")
remotes::install_github("flavjack/inti")
library(emmeans)
library(corrplot)
library(multcomp)
source('https://inkaverse.com/setup.r')
cat("Project: ", getwd())
Project: C:/Users/floza/git/prochira_maiz_morado
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14 ucrt)
os Windows 11 x64 (build 22631)
system x86_64, mingw32
ui RTerm
language (EN)
collate Spanish_Latin America.utf8
ctype Spanish_Latin America.utf8
tz America/Lima
date 2024-07-29
pandoc 3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
agricolae 1.3-7 2023-10-22 [1] CRAN (R 4.4.0)
AlgDesign 1.2.1 2022-05-25 [1] CRAN (R 4.4.0)
askpass 1.2.0 2023-09-03 [1] CRAN (R 4.4.0)
boot 1.3-30 2024-02-26 [2] CRAN (R 4.4.1)
cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.0)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.4.0)
cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.1)
cluster 2.1.6 2023-12-01 [2] CRAN (R 4.4.1)
codetools 0.2-20 2024-03-31 [2] CRAN (R 4.4.1)
colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.4.1)
corrplot * 0.92 2021-11-18 [1] CRAN (R 4.4.0)
cowplot * 1.1.3 2024-01-22 [1] CRAN (R 4.4.0)
curl 5.2.1 2024-03-01 [1] CRAN (R 4.4.0)
devtools * 2.4.5 2022-10-11 [1] CRAN (R 4.4.1)
digest 0.6.36 2024-06-23 [1] CRAN (R 4.4.1)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.0)
DT 0.33 2024-04-04 [1] CRAN (R 4.4.0)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.4.0)
emmeans * 1.10.3 2024-07-01 [1] CRAN (R 4.4.1)
estimability 1.5.1 2024-05-12 [1] CRAN (R 4.4.0)
evaluate 0.24.0 2024-06-10 [1] CRAN (R 4.4.0)
FactoMineR * 2.11 2024-04-20 [1] CRAN (R 4.4.0)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
flashClust 1.01-2 2012-08-21 [1] CRAN (R 4.4.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.0)
fs 1.6.4 2024-04-25 [1] CRAN (R 4.4.0)
gargle 1.5.2 2023-07-20 [1] CRAN (R 4.4.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.0)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.0)
ggrepel 0.9.5 2024-01-10 [1] CRAN (R 4.4.0)
glue 1.7.0 2024-01-09 [1] CRAN (R 4.4.0)
googledrive * 2.1.1 2023-06-11 [1] CRAN (R 4.4.0)
googlesheets4 * 1.1.1 2023-06-11 [1] CRAN (R 4.4.0)
gsheet * 0.4.5 2020-04-07 [1] CRAN (R 4.4.0)
gtable 0.3.5 2024-04-22 [1] CRAN (R 4.4.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.0)
httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
huito * 0.2.4 2023-10-25 [1] CRAN (R 4.4.0)
inti * 0.6.5 2024-07-29 [1] Github (flavjack/inti@38be898)
jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.4.0)
knitr * 1.48 2024-07-07 [1] CRAN (R 4.4.1)
later 1.3.2 2023-12-06 [1] CRAN (R 4.4.0)
lattice 0.22-6 2024-03-20 [2] CRAN (R 4.4.1)
leaps 3.2 2024-06-10 [1] CRAN (R 4.4.0)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
lme4 1.1-35.5 2024-07-03 [1] CRAN (R 4.4.1)
lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.4.0)
magick * 2.8.4 2024-07-14 [1] CRAN (R 4.4.1)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
MASS * 7.3-60.2 2024-04-26 [2] CRAN (R 4.4.1)
Matrix 1.7-0 2024-04-26 [2] CRAN (R 4.4.1)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.4.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.4.0)
minqa 1.2.7 2024-05-20 [1] CRAN (R 4.4.0)
mnormt 2.1.1 2022-09-26 [1] CRAN (R 4.4.0)
multcomp * 1.4-26 2024-07-18 [1] CRAN (R 4.4.1)
multcompView 0.1-10 2024-03-08 [1] CRAN (R 4.4.0)
munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.0)
mvtnorm * 1.2-5 2024-05-21 [1] CRAN (R 4.4.0)
nlme 3.1-164 2023-11-27 [2] CRAN (R 4.4.1)
nloptr 2.1.1 2024-06-25 [1] CRAN (R 4.4.1)
openssl 2.2.0 2024-05-16 [1] CRAN (R 4.4.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.0)
pkgbuild 1.4.4 2024-03-17 [1] CRAN (R 4.4.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
pkgload 1.4.0 2024-06-28 [1] CRAN (R 4.4.1)
profvis 0.3.8 2023-05-02 [1] CRAN (R 4.4.0)
promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.0)
psych * 2.4.6.26 2024-06-27 [1] CRAN (R 4.4.1)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.4.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.4.0)
Rcpp 1.0.13 2024-07-17 [1] CRAN (R 4.4.1)
readr * 2.1.5 2024-01-10 [1] CRAN (R 4.4.0)
remotes 2.5.0 2024-03-17 [1] CRAN (R 4.4.0)
rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.0)
rmarkdown 2.27 2024-05-17 [1] CRAN (R 4.4.0)
rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.4.0)
sandwich 3.1-0 2023-12-11 [1] CRAN (R 4.4.0)
scales 1.3.0 2023-11-28 [1] CRAN (R 4.4.0)
scatterplot3d 0.3-44 2023-05-05 [1] CRAN (R 4.4.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0)
shiny * 1.9.0 2024-07-29 [1] CRAN (R 4.4.1)
showtext 0.9-7 2024-03-02 [1] CRAN (R 4.4.0)
showtextdb 3.0 2020-06-04 [1] CRAN (R 4.4.0)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.0)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.4.0)
survival * 3.6-4 2024-04-24 [2] CRAN (R 4.4.1)
sysfonts 0.8.9 2024-03-02 [1] CRAN (R 4.4.0)
TH.data * 1.1-2 2023-04-17 [1] CRAN (R 4.4.0)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.4.0)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.0)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.4.0)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.4.0)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.4.0)
urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.4.0)
usethis * 3.0.0 2024-07-29 [1] CRAN (R 4.4.1)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.0)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
withr 3.0.0 2024-01-16 [1] CRAN (R 4.4.0)
xfun 0.46 2024-07-18 [1] CRAN (R 4.4.1)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.0)
yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.1)
zoo 1.8-12 2023-04-13 [1] CRAN (R 4.4.0)
[1] C:/Users/floza/AppData/Local/R/win-library/4.4
[2] C:/Program Files/R/R-4.4.1/library
──────────────────────────────────────────────────────────────────────────────
Refrencias
- (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
- (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
- (HCPC) https://youtu.be/EJqYTDTJJug
Import data
https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741
url <- "https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741"
gs <- url %>%
as_sheets_id()
imbibition <- gs %>%
range_read("imbibition") %>%
rename_with(~ tolower(.)) %>%
mutate(time = tiempo, .after = tiempo) %>%
mutate(variedad = case_when(
variedad %in% c("criollo") ~ "Creole"
, variedad %in% c("Hibrido") ~ "Hybrid"
)) %>%
mutate(across(1:tiempo, ~ as.factor(.)))
str(imbibition)
## tibble [2,100 × 7] (S3: tbl_df/tbl/data.frame)
## $ bloque : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ trat : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ variedad : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
## $ tiempo : Factor w/ 5 levels "0","3","6","9",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ time : num [1:2100] 0 0 0 0 0 0 0 0 0 0 ...
## $ peso : num [1:2100] 0.58 0.62 0.73 0.72 0.72 0.68 0.71 0.61 0.69 0.64 ...
germination <- gs %>%
range_read("germination") %>%
rename_with(~ tolower(.)) %>%
mutate(variedad = case_when(
variedad %in% c("criollo") ~ "Creole"
, variedad %in% c("Hibrido") ~ "Hybrid"
)) %>%
mutate(trat = case_when(
tratamiento %in% "Agua Destilada" ~ "T0"
, tratamiento %in% "Algas Marinas 1 L/cil" ~ "T1"
, tratamiento %in% "Algas Marinas 1,5 L/cil" ~ "T2"
, tratamiento %in% "Azufre 100 gr.200 L-1" ~ "T3"
, tratamiento %in% "Azufre 150 gr.200 L-1" ~ "T4"
, tratamiento %in% "Suero de leche 10%" ~ "T5"
, tratamiento %in% "Suero de leche 30%" ~ "T6"
), .before = tratamiento) %>%
mutate(across(1:variedad, ~ as.factor(.)))
str(germination)
## tibble [42 × 11] (S3: tbl_df/tbl/data.frame)
## $ bloque : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
## $ trat : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ variedad : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
## $ dia 1 : num [1:42] 2 4 3 0 1 1 0 0 1 0 ...
## $ dia 2 : num [1:42] 5 4 5 3 2 1 1 4 5 1 ...
## $ dia 3 : num [1:42] 1 1 1 1 0 0 0 0 0 0 ...
## $ total : num [1:42] 8 9 9 4 3 2 1 4 6 1 ...
## $ pg : num [1:42] 80 90 90 40 30 20 10 40 60 10 ...
## $ vg : num [1:42] 2.67 3 3 2 1.5 ...
## $ ig : num [1:42] 2.4 2.7 2.7 0.8 0.6 0.4 0.1 0.4 1.2 0.1 ...
plantula <- gs %>%
range_read("plantula") %>%
rename_with(~ tolower(.)) %>%
mutate(variedad = case_when(
variedad %in% c("criollo") ~ "Creole"
, variedad %in% c("hibrido") ~ "Hybrid"
)) %>%
mutate(across(1:variedad, ~ as.factor(.)))
str(plantula)
## tibble [210 × 16] (S3: tbl_df/tbl/data.frame)
## $ trat : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ tratamiento : Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ variedad : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
## $ raiz_lgtd : num [1:210] 11 8 12 11 8 13 10 12 9 13 ...
## $ gsr_raiz : num [1:210] 1.3 1.19 1.51 1.21 1.17 1.13 1.68 1.27 1.03 1.16 ...
## $ num_raiz : num [1:210] 8 11 11 9 12 16 10 9 16 11 ...
## $ peso_fres_raiz : num [1:210] 4.82 3.21 4.91 4.42 4.62 6.07 4.97 6.13 3.05 4 ...
## $ peso_seco_raiz : num [1:210] 0.73 0.41 0.62 0.66 0.72 0.54 0.75 0.56 0.57 0.74 ...
## $ alt_planta : num [1:210] 30 26 28 32 25 27 28 35 29 29 ...
## $ gsr_tallo : num [1:210] 5.86 4.56 6.59 4.63 4.55 4.14 4.02 4.32 3.45 3.61 ...
## $ nhp_hoja : num [1:210] 5 5 5 6 4 5 5 5 5 5 ...
## $ larg_hoja : num [1:210] 26 23 21 27 29 22 24 30 25 23 ...
## $ grs_hoja : num [1:210] 0.94 1.15 0.89 0.98 1.01 0.72 0.62 1.03 0.71 1.34 ...
## $ anch_hoja : num [1:210] 19.3 19.9 21.5 17.3 18.9 ...
## $ peso_fres_brote: num [1:210] 5.34 5.99 5.45 4.81 7.03 6.79 4.99 4.53 3.56 4 ...
## $ peso_seco_brote: num [1:210] 0.5 0.49 1.04 0.78 0.68 0.67 0.69 0.78 0.73 0.75 ...
Tratamientos
imbibition %>%
group_by(trat, tratamiento) %>%
summarise(n = n()) %>%
select(!n)
## # A tibble: 7 × 2
## # Groups: trat [7]
## trat tratamiento
## <fct> <fct>
## 1 T0 Agua Destilada
## 2 T1 Algas Marinas 1 L/cil
## 3 T2 Algas Marinas 1,5 L/cil
## 4 T3 Azufre 100 gr.200 L-1
## 5 T4 Azufre 150 gr.200 L-1
## 6 T5 Suero de leche 10%
## 7 T6 Suero de leche 30%
Data summary
sm <- imbibition %>%
group_by(tratamiento, variedad, tiempo) %>%
summarise(across(peso, ~ sum(!is.na(.))))
sm
## # A tibble: 70 × 4
## # Groups: tratamiento, variedad [14]
## tratamiento variedad tiempo peso
## <fct> <fct> <fct> <int>
## 1 Agua Destilada Creole 0 30
## 2 Agua Destilada Creole 3 30
## 3 Agua Destilada Creole 6 30
## 4 Agua Destilada Creole 9 30
## 5 Agua Destilada Creole 12 30
## 6 Agua Destilada Hybrid 0 30
## 7 Agua Destilada Hybrid 3 30
## 8 Agua Destilada Hybrid 6 30
## 9 Agua Destilada Hybrid 9 30
## 10 Agua Destilada Hybrid 12 30
## # ℹ 60 more rows
sm <- germination %>%
group_by(tratamiento, variedad) %>%
summarise(across(pg:ig, ~ sum(!is.na(.))))
sm
## # A tibble: 14 × 5
## # Groups: tratamiento [7]
## tratamiento variedad pg vg ig
## <fct> <fct> <int> <int> <int>
## 1 Agua Destilada Creole 3 3 3
## 2 Agua Destilada Hybrid 3 3 3
## 3 Algas Marinas 1 L/cil Creole 3 3 3
## 4 Algas Marinas 1 L/cil Hybrid 3 3 3
## 5 Algas Marinas 1,5 L/cil Creole 3 3 3
## 6 Algas Marinas 1,5 L/cil Hybrid 3 3 3
## 7 Azufre 100 gr.200 L-1 Creole 3 3 3
## 8 Azufre 100 gr.200 L-1 Hybrid 3 3 3
## 9 Azufre 150 gr.200 L-1 Creole 3 3 3
## 10 Azufre 150 gr.200 L-1 Hybrid 3 3 3
## 11 Suero de leche 10% Creole 3 3 3
## 12 Suero de leche 10% Hybrid 3 3 3
## 13 Suero de leche 30% Creole 3 3 3
## 14 Suero de leche 30% Hybrid 3 3 3
sm <- plantula %>%
group_by(tratamiento, variedad) %>%
summarise(across(where(is.numeric), ~ sum(!is.na(.))))
sm
## # A tibble: 14 × 15
## # Groups: tratamiento [7]
## tratamiento variedad raiz_lgtd gsr_raiz num_raiz peso_fres_raiz
## <fct> <fct> <int> <int> <int> <int>
## 1 Agua Destilada Creole 15 15 15 15
## 2 Agua Destilada Hybrid 15 15 15 15
## 3 Algas Marinas 1 L/cil Creole 15 15 15 15
## 4 Algas Marinas 1 L/cil Hybrid 15 15 15 15
## 5 Algas Marinas 1,5 L/cil Creole 15 15 15 15
## 6 Algas Marinas 1,5 L/cil Hybrid 15 15 15 15
## 7 Azufre 100 gr.200 L-1 Creole 15 15 15 15
## 8 Azufre 100 gr.200 L-1 Hybrid 15 15 15 15
## 9 Azufre 150 gr.200 L-1 Creole 15 15 15 15
## 10 Azufre 150 gr.200 L-1 Hybrid 15 15 15 15
## 11 Suero de leche 10% Creole 15 15 15 15
## 12 Suero de leche 10% Hybrid 15 15 15 15
## 13 Suero de leche 30% Creole 15 15 15 15
## 14 Suero de leche 30% Hybrid 15 15 15 15
## # ℹ 9 more variables: peso_seco_raiz <int>, alt_planta <int>, gsr_tallo <int>,
## # nhp_hoja <int>, larg_hoja <int>, grs_hoja <int>, anch_hoja <int>,
## # peso_fres_brote <int>, peso_seco_brote <int>
Objetivos
Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.
Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.
Objetivo Específico 1
Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.
- Imbibiciación, % germinación, velocidad e IG
Imbibición
trait <- "peso"
fb <- imbibition
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad + (1 + tiempo|tratamiento)") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + tiempo + trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers %>% kable()
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: peso
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 0.0021 0.00105 0.1222 0.885
## tiempo 4 10.0058 2.50146 289.7715 <0.0000000000000002 ***
## trat 6 3.2174 0.53624 62.1186 <0.0000000000000002 ***
## variedad 1 0.6165 0.61649 71.4150 <0.0000000000000002 ***
## trat:variedad 6 2.6467 0.44111 51.0987 <0.0000000000000002 ***
## Residuals 2080 17.9556 0.00863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ tiempo|variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()
| 1 |
12 |
Creole |
T0 |
0.8296986 |
0.0086019 |
2080 |
0.8128293 |
0.8465678 |
a |
| 3 |
9 |
Creole |
T0 |
0.8279129 |
0.0086019 |
2080 |
0.8110436 |
0.8447821 |
a |
| 2 |
3 |
Creole |
T0 |
0.7645081 |
0.0086019 |
2080 |
0.7476388 |
0.7813774 |
b |
| 4 |
6 |
Creole |
T0 |
0.7620295 |
0.0086019 |
2080 |
0.7451603 |
0.7788988 |
b |
| 5 |
0 |
Creole |
T0 |
0.6398176 |
0.0086019 |
2080 |
0.6229483 |
0.6566869 |
c |
| 11 |
12 |
Creole |
T1 |
0.7157719 |
0.0086019 |
2080 |
0.6989026 |
0.7326412 |
a |
| 13 |
9 |
Creole |
T1 |
0.7139862 |
0.0086019 |
2080 |
0.6971169 |
0.7308555 |
a |
| 12 |
3 |
Creole |
T1 |
0.6505814 |
0.0086019 |
2080 |
0.6337122 |
0.6674507 |
b |
| 14 |
6 |
Creole |
T1 |
0.6481029 |
0.0086019 |
2080 |
0.6312336 |
0.6649721 |
b |
| 15 |
0 |
Creole |
T1 |
0.5258910 |
0.0086019 |
2080 |
0.5090217 |
0.5427602 |
c |
| 21 |
12 |
Creole |
T2 |
0.6749719 |
0.0086019 |
2080 |
0.6581026 |
0.6918412 |
a |
| 23 |
9 |
Creole |
T2 |
0.6731862 |
0.0086019 |
2080 |
0.6563169 |
0.6900555 |
a |
| 22 |
3 |
Creole |
T2 |
0.6097814 |
0.0086019 |
2080 |
0.5929122 |
0.6266507 |
b |
| 24 |
6 |
Creole |
T2 |
0.6073029 |
0.0086019 |
2080 |
0.5904336 |
0.6241721 |
b |
| 25 |
0 |
Creole |
T2 |
0.4850910 |
0.0086019 |
2080 |
0.4682217 |
0.5019602 |
c |
| 31 |
12 |
Creole |
T3 |
0.6591052 |
0.0086019 |
2080 |
0.6422360 |
0.6759745 |
a |
| 33 |
9 |
Creole |
T3 |
0.6573195 |
0.0086019 |
2080 |
0.6404503 |
0.6741888 |
a |
| 32 |
3 |
Creole |
T3 |
0.5939148 |
0.0086019 |
2080 |
0.5770455 |
0.6107840 |
b |
| 34 |
6 |
Creole |
T3 |
0.5914362 |
0.0086019 |
2080 |
0.5745669 |
0.6083055 |
b |
| 35 |
0 |
Creole |
T3 |
0.4692243 |
0.0086019 |
2080 |
0.4523550 |
0.4860936 |
c |
| 41 |
12 |
Creole |
T4 |
0.6322386 |
0.0086019 |
2080 |
0.6153693 |
0.6491078 |
a |
| 43 |
9 |
Creole |
T4 |
0.6304529 |
0.0086019 |
2080 |
0.6135836 |
0.6473221 |
a |
| 42 |
3 |
Creole |
T4 |
0.5670481 |
0.0086019 |
2080 |
0.5501788 |
0.5839174 |
b |
| 44 |
6 |
Creole |
T4 |
0.5645695 |
0.0086019 |
2080 |
0.5477003 |
0.5814388 |
b |
| 45 |
0 |
Creole |
T4 |
0.4423576 |
0.0086019 |
2080 |
0.4254883 |
0.4592269 |
c |
| 51 |
12 |
Creole |
T5 |
0.8092386 |
0.0086019 |
2080 |
0.7923693 |
0.8261078 |
a |
| 53 |
9 |
Creole |
T5 |
0.8074529 |
0.0086019 |
2080 |
0.7905836 |
0.8243221 |
a |
| 52 |
3 |
Creole |
T5 |
0.7440481 |
0.0086019 |
2080 |
0.7271788 |
0.7609174 |
b |
| 54 |
6 |
Creole |
T5 |
0.7415695 |
0.0086019 |
2080 |
0.7247003 |
0.7584388 |
b |
| 55 |
0 |
Creole |
T5 |
0.6193576 |
0.0086019 |
2080 |
0.6024883 |
0.6362269 |
c |
| 61 |
12 |
Creole |
T6 |
0.7740386 |
0.0086019 |
2080 |
0.7571693 |
0.7909078 |
a |
| 63 |
9 |
Creole |
T6 |
0.7722529 |
0.0086019 |
2080 |
0.7553836 |
0.7891221 |
a |
| 62 |
3 |
Creole |
T6 |
0.7088481 |
0.0086019 |
2080 |
0.6919788 |
0.7257174 |
b |
| 64 |
6 |
Creole |
T6 |
0.7063695 |
0.0086019 |
2080 |
0.6895003 |
0.7232388 |
b |
| 65 |
0 |
Creole |
T6 |
0.5841576 |
0.0086019 |
2080 |
0.5672883 |
0.6010269 |
c |
| 6 |
12 |
Hybrid |
T0 |
0.7764386 |
0.0086019 |
2080 |
0.7595693 |
0.7933078 |
a |
| 8 |
9 |
Hybrid |
T0 |
0.7746529 |
0.0086019 |
2080 |
0.7577836 |
0.7915221 |
a |
| 7 |
3 |
Hybrid |
T0 |
0.7112481 |
0.0086019 |
2080 |
0.6943788 |
0.7281174 |
b |
| 9 |
6 |
Hybrid |
T0 |
0.7087695 |
0.0086019 |
2080 |
0.6919003 |
0.7256388 |
b |
| 10 |
0 |
Hybrid |
T0 |
0.5865576 |
0.0086019 |
2080 |
0.5696883 |
0.6034269 |
c |
| 16 |
12 |
Hybrid |
T1 |
0.7279719 |
0.0086019 |
2080 |
0.7111026 |
0.7448412 |
a |
| 18 |
9 |
Hybrid |
T1 |
0.7261862 |
0.0086019 |
2080 |
0.7093169 |
0.7430555 |
a |
| 17 |
3 |
Hybrid |
T1 |
0.6627814 |
0.0086019 |
2080 |
0.6459122 |
0.6796507 |
b |
| 19 |
6 |
Hybrid |
T1 |
0.6603029 |
0.0086019 |
2080 |
0.6434336 |
0.6771721 |
b |
| 20 |
0 |
Hybrid |
T1 |
0.5380910 |
0.0086019 |
2080 |
0.5212217 |
0.5549602 |
c |
| 26 |
12 |
Hybrid |
T2 |
0.7881052 |
0.0086019 |
2080 |
0.7712360 |
0.8049745 |
a |
| 28 |
9 |
Hybrid |
T2 |
0.7863195 |
0.0086019 |
2080 |
0.7694503 |
0.8031888 |
a |
| 27 |
3 |
Hybrid |
T2 |
0.7229148 |
0.0086019 |
2080 |
0.7060455 |
0.7397840 |
b |
| 29 |
6 |
Hybrid |
T2 |
0.7204362 |
0.0086019 |
2080 |
0.7035669 |
0.7373055 |
b |
| 30 |
0 |
Hybrid |
T2 |
0.5982243 |
0.0086019 |
2080 |
0.5813550 |
0.6150936 |
c |
| 36 |
12 |
Hybrid |
T3 |
0.7332386 |
0.0086019 |
2080 |
0.7163693 |
0.7501078 |
a |
| 38 |
9 |
Hybrid |
T3 |
0.7314529 |
0.0086019 |
2080 |
0.7145836 |
0.7483221 |
a |
| 37 |
3 |
Hybrid |
T3 |
0.6680481 |
0.0086019 |
2080 |
0.6511788 |
0.6849174 |
b |
| 39 |
6 |
Hybrid |
T3 |
0.6655695 |
0.0086019 |
2080 |
0.6487003 |
0.6824388 |
b |
| 40 |
0 |
Hybrid |
T3 |
0.5433576 |
0.0086019 |
2080 |
0.5264883 |
0.5602269 |
c |
| 46 |
12 |
Hybrid |
T4 |
0.7735052 |
0.0086019 |
2080 |
0.7566360 |
0.7903745 |
a |
| 48 |
9 |
Hybrid |
T4 |
0.7717195 |
0.0086019 |
2080 |
0.7548503 |
0.7885888 |
a |
| 47 |
3 |
Hybrid |
T4 |
0.7083148 |
0.0086019 |
2080 |
0.6914455 |
0.7251840 |
b |
| 49 |
6 |
Hybrid |
T4 |
0.7058362 |
0.0086019 |
2080 |
0.6889669 |
0.7227055 |
b |
| 50 |
0 |
Hybrid |
T4 |
0.5836243 |
0.0086019 |
2080 |
0.5667550 |
0.6004936 |
c |
| 56 |
12 |
Hybrid |
T5 |
0.7615719 |
0.0086019 |
2080 |
0.7447026 |
0.7784412 |
a |
| 58 |
9 |
Hybrid |
T5 |
0.7597862 |
0.0086019 |
2080 |
0.7429169 |
0.7766555 |
a |
| 57 |
3 |
Hybrid |
T5 |
0.6963814 |
0.0086019 |
2080 |
0.6795122 |
0.7132507 |
b |
| 59 |
6 |
Hybrid |
T5 |
0.6939029 |
0.0086019 |
2080 |
0.6770336 |
0.7107721 |
b |
| 60 |
0 |
Hybrid |
T5 |
0.5716910 |
0.0086019 |
2080 |
0.5548217 |
0.5885602 |
c |
| 66 |
12 |
Hybrid |
T6 |
0.7741052 |
0.0086019 |
2080 |
0.7572360 |
0.7909745 |
a |
| 68 |
9 |
Hybrid |
T6 |
0.7723195 |
0.0086019 |
2080 |
0.7554503 |
0.7891888 |
a |
| 67 |
3 |
Hybrid |
T6 |
0.7089148 |
0.0086019 |
2080 |
0.6920455 |
0.7257840 |
b |
| 69 |
6 |
Hybrid |
T6 |
0.7064362 |
0.0086019 |
2080 |
0.6895669 |
0.7233055 |
b |
| 70 |
0 |
Hybrid |
T6 |
0.5842243 |
0.0086019 |
2080 |
0.5673550 |
0.6010936 |
c |
p1a <- mc %>%
plot_smr(type = "line"
, x = "tiempo"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Seed weight (g)"
, xlab = "Time (h)"
, glab = "Variety"
, ylimits = c(0, 1, 0.2)
) +
facet_wrap(. ~ trat, ncol = 2)
p1a
Porcentaje de Germination
trait <- "pg"
fb <- germination
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers %>% kable()
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: pg
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 633.3 316.7 0.6222 0.544582
## trat 6 7000.0 1166.7 2.2922 0.065673 .
## variedad 1 4609.5 4609.5 9.0565 0.005753 **
## trat:variedad 6 6857.1 1142.9 2.2454 0.070466 .
## Residuals 26 13233.3 509.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()
| 2 |
Creole |
T0 |
86.66667 |
13.02529 |
26 |
59.8928044 |
113.44053 |
a |
| 1 |
Hybrid |
T0 |
63.33333 |
13.02529 |
26 |
36.5594710 |
90.10720 |
a |
| 3 |
Hybrid |
T1 |
70.00000 |
13.02529 |
26 |
43.2261377 |
96.77386 |
a |
| 4 |
Creole |
T1 |
30.00000 |
13.02529 |
26 |
3.2261377 |
56.77386 |
b |
| 5 |
Hybrid |
T2 |
56.66667 |
13.02529 |
26 |
29.8928044 |
83.44053 |
a |
| 6 |
Creole |
T2 |
36.66667 |
13.02529 |
26 |
9.8928044 |
63.44053 |
a |
| 7 |
Hybrid |
T3 |
66.66667 |
13.02529 |
26 |
39.8928044 |
93.44053 |
a |
| 8 |
Creole |
T3 |
16.66667 |
13.02529 |
26 |
-10.1071956 |
43.44053 |
b |
| 9 |
Hybrid |
T4 |
76.66667 |
13.02529 |
26 |
49.8928044 |
103.44053 |
a |
| 10 |
Creole |
T4 |
26.66667 |
13.02529 |
26 |
-0.1071956 |
53.44053 |
b |
| 11 |
Hybrid |
T5 |
70.00000 |
13.02529 |
26 |
43.2261377 |
96.77386 |
a |
| 12 |
Creole |
T5 |
70.00000 |
13.02529 |
26 |
43.2261377 |
96.77386 |
a |
| 13 |
Hybrid |
T6 |
43.33333 |
13.02529 |
26 |
16.5594710 |
70.10720 |
a |
| 14 |
Creole |
T6 |
33.33333 |
13.02529 |
26 |
6.5594710 |
60.10720 |
a |
p1b <- mc %>%
plot_smr(type = "bar"
, x = "trat"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Germination ('%')"
, xlab = "Treatments"
, glab = "Variety"
, ylimits = c(0, 120, 20)
)
p1b
Velocidad de germinación
trait <- "vg"
fb <- germination
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers %>% kable()
| 7 |
7 |
1 |
T2 |
Creole |
1 |
-1.666667 |
-3.372454 |
0.0007450 |
0.0007450159 |
0.0305456 |
OUTLIER |
| 34 |
34 |
1 |
T4 |
Hybrid |
5 |
1.888889 |
3.822114 |
0.0001323 |
0.0001323123 |
0.0055571 |
OUTLIER |
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: vg
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 1.3321 0.66607 1.3664 0.274150
## trat 6 8.6594 1.44323 2.9608 0.026214 *
## variedad 1 2.0003 2.00025 4.1035 0.054051 .
## trat:variedad 6 11.5622 1.92703 3.9533 0.006872 **
## Residuals 24 11.6989 0.48745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()
| 2 |
Creole |
T0 |
2.888889 |
0.4030931 |
24 |
2.0569455 |
3.720832 |
a |
| 1 |
Hybrid |
T0 |
2.333333 |
0.4030931 |
24 |
1.5013900 |
3.165277 |
a |
| 3 |
Hybrid |
T1 |
3.277778 |
0.4030931 |
24 |
2.4458344 |
4.109721 |
a |
| 4 |
Creole |
T1 |
1.500000 |
0.4030931 |
24 |
0.6680567 |
2.331943 |
b |
| 6 |
Creole |
T2 |
3.379630 |
0.5004960 |
24 |
2.3466566 |
4.412603 |
a |
| 5 |
Hybrid |
T2 |
2.833333 |
0.4030931 |
24 |
2.0013900 |
3.665277 |
a |
| 7 |
Hybrid |
T3 |
3.833333 |
0.4030931 |
24 |
3.0013900 |
4.665277 |
a |
| 8 |
Creole |
T3 |
1.666667 |
0.4030931 |
24 |
0.8347233 |
2.498610 |
b |
| 9 |
Hybrid |
T4 |
2.046296 |
0.5004960 |
24 |
1.0133232 |
3.079269 |
a |
| 10 |
Creole |
T4 |
1.333333 |
0.4030931 |
24 |
0.5013900 |
2.165277 |
a |
| 12 |
Creole |
T5 |
3.055556 |
0.4030931 |
24 |
2.2236122 |
3.887499 |
a |
| 11 |
Hybrid |
T5 |
3.000000 |
0.4030931 |
24 |
2.1680567 |
3.831943 |
a |
| 14 |
Creole |
T6 |
2.333333 |
0.4030931 |
24 |
1.5013900 |
3.165277 |
a |
| 13 |
Hybrid |
T6 |
1.833333 |
0.4030931 |
24 |
1.0013900 |
2.665277 |
a |
p1c <- mc %>%
plot_smr(type = "bar"
, x = "trat"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Germination speed (days)"
, xlab = "Treatments"
, glab = "Variety"
, ylimits = c(0, 6, 1)
)
p1c
Indice de germinación
trait <- "ig"
fb <- germination
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers %>% kable()
| 25 |
25 |
1 |
T1 |
Hybrid |
0.2 |
-1.466667 |
-3.29751 |
0.0009755 |
0.0009754607 |
0.0409693 |
OUTLIER |
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ig
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 0.3050 0.1525 0.4149 0.664896
## trat 6 10.3540 1.7257 4.6949 0.002507 **
## variedad 1 3.8850 3.8850 10.5697 0.003278 **
## trat:variedad 6 6.5489 1.0915 2.9695 0.024965 *
## Residuals 25 9.1890 0.3676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()
| 2 |
Creole |
T0 |
2.6000000 |
0.3500293 |
25 |
1.8791012 |
3.3208988 |
a |
| 1 |
Hybrid |
T0 |
1.7666667 |
0.3500293 |
25 |
1.0457678 |
2.4875655 |
a |
| 3 |
Hybrid |
T1 |
2.3679487 |
0.4341579 |
25 |
1.4737838 |
3.2621137 |
a |
| 4 |
Creole |
T1 |
0.6000000 |
0.3500293 |
25 |
-0.1208988 |
1.3208988 |
b |
| 5 |
Hybrid |
T2 |
1.1333333 |
0.3500293 |
25 |
0.4124345 |
1.8542322 |
a |
| 6 |
Creole |
T2 |
0.5666667 |
0.3500293 |
25 |
-0.1542322 |
1.2875655 |
a |
| 7 |
Hybrid |
T3 |
1.2333333 |
0.3500293 |
25 |
0.5124345 |
1.9542322 |
a |
| 8 |
Creole |
T3 |
0.1666667 |
0.3500293 |
25 |
-0.5542322 |
0.8875655 |
b |
| 9 |
Hybrid |
T4 |
1.9666667 |
0.3500293 |
25 |
1.2457678 |
2.6875655 |
a |
| 10 |
Creole |
T4 |
0.5333333 |
0.3500293 |
25 |
-0.1875655 |
1.2542322 |
b |
| 11 |
Hybrid |
T5 |
1.7000000 |
0.3500293 |
25 |
0.9791012 |
2.4208988 |
a |
| 12 |
Creole |
T5 |
1.6666667 |
0.3500293 |
25 |
0.9457678 |
2.3875655 |
a |
| 13 |
Hybrid |
T6 |
1.0666667 |
0.3500293 |
25 |
0.3457678 |
1.7875655 |
a |
| 14 |
Creole |
T6 |
0.5333333 |
0.3500293 |
25 |
-0.1875655 |
1.2542322 |
a |
p1d <- mc %>%
plot_smr(type = "bar"
, x = "trat"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Germination Index"
, xlab = "Treatments"
, glab = "Variety"
, ylimits = c(0, 5, 1)
)
p1d
Figura 1
legend <- cowplot::get_plot_component(p1b, 'guide-box-top', return_all = TRUE)
p1i <- list(p1b + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p1c + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p1d + labs(x = NULL) + theme(legend.position="none")
) %>%
plot_grid(plotlist = ., ncol = 1
, labels = c("b", "c", "d")
)
p1il <- list(legend, p1i) %>%
plot_grid(plotlist = ., ncol = 1, align = 'v', rel_heights = c(0.05, 1))
plot <- list(p1a, p1il) %>%
plot_grid(plotlist = .
, ncol = 2
, rel_widths = c(1.5, 1)
, labels = c("a")
, label_y = 0.96
)
plot %>%
ggsave2(plot = ., "files/Fig-1.jpg"
, units = "cm"
, width = 30
, height = 25
)
plot %>%
ggsave2(plot = ., "files/Fig-1.eps"
, units = "cm"
, width = 30
, height = 25
)
knitr::include_graphics("files/Fig-1.jpg")
Objetivo Específico 2
Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.
fb <- plantula
rsl <- 4:length(fb) %>% map(\(x) {
trait <- names(fb)[x]
cat("\n### ", trait)
lmm <- paste({{trait}}, "~ 1 + (1|trat) + trat*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
cat("\n#### ", "Diagnostico")
rmout$diagplot %>% print()
cat("\n#### ", "Outliers")
rmout$outliers %>% kable() %>% print()
cat("\n#### ", "ANOVA")
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model) %>% anova_table %>% kable() %>% print()
cat("\n#### ", "Mean comparison")
mc <- emmeans(model, ~ variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group") %>%
rename({{trait}} := "emmean")
mc %>% kable() %>% print()
plot <- mc %>%
plot_smr(x = "trat"
, y = trait
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Treatments"
, glab = "Variety"
)
plot
list(mc = mc, plot = plot)
})
raiz_lgtd
Diagnostico
ANOVA
| trat |
6 |
113.028571428572 |
18.8380952380953 |
2.20354897748071 |
0.0442610263913077 |
* |
| variedad |
1 |
2.30476190476198 |
2.30476190476198 |
0.269594970955686 |
0.604189403305695 |
ns |
| trat:variedad |
6 |
240.761904761907 |
40.1269841269844 |
4.69377470093635 |
0.000173605924663632 |
*** |
| Residuals |
196 |
1675.6 |
8.54897959183673 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 1 |
Hybrid |
T0 |
16.00000 |
0.7549384 |
196 |
14.511155 |
17.48885 |
a |
| 2 |
Creole |
T0 |
11.00000 |
0.7549384 |
196 |
9.511155 |
12.48885 |
b |
| 4 |
Creole |
T1 |
14.00000 |
0.7549384 |
196 |
12.511155 |
15.48885 |
a |
| 3 |
Hybrid |
T1 |
13.93333 |
0.7549384 |
196 |
12.444488 |
15.42218 |
a |
| 5 |
Hybrid |
T2 |
13.60000 |
0.7549384 |
196 |
12.111155 |
15.08885 |
a |
| 6 |
Creole |
T2 |
13.33333 |
0.7549384 |
196 |
11.844488 |
14.82218 |
a |
| 8 |
Creole |
T3 |
12.93333 |
0.7549384 |
196 |
11.444488 |
14.42218 |
a |
| 7 |
Hybrid |
T3 |
12.46667 |
0.7549384 |
196 |
10.977821 |
13.95551 |
a |
| 10 |
Creole |
T4 |
15.13333 |
0.7549384 |
196 |
13.644488 |
16.62218 |
a |
| 9 |
Hybrid |
T4 |
14.00000 |
0.7549384 |
196 |
12.511155 |
15.48885 |
a |
| 12 |
Creole |
T5 |
15.80000 |
0.7549384 |
196 |
14.311155 |
17.28885 |
a |
| 11 |
Hybrid |
T5 |
13.40000 |
0.7549384 |
196 |
11.911155 |
14.88885 |
b |
| 13 |
Hybrid |
T6 |
15.06667 |
0.7549384 |
196 |
13.577822 |
16.55551 |
a |
| 14 |
Creole |
T6 |
14.80000 |
0.7549384 |
196 |
13.311155 |
16.28885 |
a |
gsr_raiz
Diagnostico
ANOVA
| trat |
6 |
2.85575333333333 |
0.475958888888889 |
12.0927440494915 |
0.0000000000150374615277643 |
*** |
| variedad |
1 |
0.327257619047619 |
0.327257619047619 |
8.3146732160573 |
0.00437199311126879 |
** |
| trat:variedad |
6 |
0.701705714285716 |
0.116950952380953 |
2.97138674474315 |
0.00845464188145274 |
** |
| Residuals |
196 |
7.71437333333333 |
0.0393590476190476 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
1.2560000 |
0.0512244 |
196 |
1.1549783 |
1.3570217 |
a |
| 1 |
Hybrid |
T0 |
1.0853333 |
0.0512244 |
196 |
0.9843116 |
1.1863550 |
b |
| 3 |
Hybrid |
T1 |
0.8926667 |
0.0512244 |
196 |
0.7916450 |
0.9936884 |
a |
| 4 |
Creole |
T1 |
0.7486667 |
0.0512244 |
196 |
0.6476450 |
0.8496884 |
b |
| 5 |
Hybrid |
T2 |
0.9500000 |
0.0512244 |
196 |
0.8489783 |
1.0510217 |
a |
| 6 |
Creole |
T2 |
0.9260000 |
0.0512244 |
196 |
0.8249783 |
1.0270217 |
a |
| 8 |
Creole |
T3 |
0.8600000 |
0.0512244 |
196 |
0.7589783 |
0.9610217 |
a |
| 7 |
Hybrid |
T3 |
0.7760000 |
0.0512244 |
196 |
0.6749783 |
0.8770217 |
a |
| 10 |
Creole |
T4 |
0.9846667 |
0.0512244 |
196 |
0.8836450 |
1.0856884 |
a |
| 9 |
Hybrid |
T4 |
0.7840000 |
0.0512244 |
196 |
0.6829783 |
0.8850217 |
b |
| 12 |
Creole |
T5 |
1.1066667 |
0.0512244 |
196 |
1.0056450 |
1.2076884 |
a |
| 11 |
Hybrid |
T5 |
0.9280000 |
0.0512244 |
196 |
0.8269783 |
1.0290217 |
b |
| 14 |
Creole |
T6 |
1.0513333 |
0.0512244 |
196 |
0.9503116 |
1.1523550 |
a |
| 13 |
Hybrid |
T6 |
0.9646667 |
0.0512244 |
196 |
0.8636450 |
1.0656884 |
a |
num_raiz
Diagnostico
ANOVA
| trat |
6 |
457.161904761906 |
76.193650793651 |
9.76160594968335 |
0.00000000208792569898294 |
*** |
| variedad |
1 |
4.28571428571413 |
4.28571428571413 |
0.549067456858964 |
0.45958580814979 |
ns |
| trat:variedad |
6 |
153.714285714286 |
25.6190476190477 |
3.2822032421126 |
0.00424059446258091 |
** |
| Residuals |
196 |
1529.86666666667 |
7.80544217687077 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 1 |
Hybrid |
T0 |
11.33333 |
0.7213618 |
196 |
9.910706 |
12.75596 |
a |
| 2 |
Creole |
T0 |
10.86667 |
0.7213618 |
196 |
9.444039 |
12.28929 |
a |
| 3 |
Hybrid |
T1 |
15.73333 |
0.7213618 |
196 |
14.310706 |
17.15596 |
a |
| 4 |
Creole |
T1 |
14.53333 |
0.7213618 |
196 |
13.110706 |
15.95596 |
a |
| 6 |
Creole |
T2 |
15.06667 |
0.7213618 |
196 |
13.644039 |
16.48929 |
a |
| 5 |
Hybrid |
T2 |
12.00000 |
0.7213618 |
196 |
10.577373 |
13.42263 |
b |
| 7 |
Hybrid |
T3 |
12.53333 |
0.7213618 |
196 |
11.110706 |
13.95596 |
a |
| 8 |
Creole |
T3 |
10.66667 |
0.7213618 |
196 |
9.244039 |
12.08929 |
a |
| 10 |
Creole |
T4 |
13.40000 |
0.7213618 |
196 |
11.977373 |
14.82263 |
a |
| 9 |
Hybrid |
T4 |
10.93333 |
0.7213618 |
196 |
9.510706 |
12.35596 |
b |
| 11 |
Hybrid |
T5 |
11.80000 |
0.7213618 |
196 |
10.377373 |
13.22263 |
a |
| 12 |
Creole |
T5 |
11.33333 |
0.7213618 |
196 |
9.910706 |
12.75596 |
a |
| 14 |
Creole |
T6 |
10.73333 |
0.7213618 |
196 |
9.310706 |
12.15596 |
a |
| 13 |
Hybrid |
T6 |
10.26667 |
0.7213618 |
196 |
8.844039 |
11.68929 |
a |
peso_fres_raiz
Diagnostico
ANOVA
| trat |
6 |
73.5104057142858 |
12.2517342857143 |
6.56141211693855 |
0.00000248176481747279 |
*** |
| variedad |
1 |
0.00092190476190473 |
0.00092190476190473 |
0.000493725780723001 |
0.982295115363446 |
ns |
| trat:variedad |
6 |
36.2561847619047 |
6.04269746031745 |
3.23616456335913 |
0.00469927715193408 |
** |
| Residuals |
196 |
365.979133333333 |
1.86724047619048 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
4.698000 |
0.3528211 |
196 |
4.002187 |
5.393813 |
a |
| 1 |
Hybrid |
T0 |
4.537333 |
0.3528211 |
196 |
3.841520 |
5.233146 |
a |
| 4 |
Creole |
T1 |
6.624000 |
0.3528211 |
196 |
5.928187 |
7.319813 |
a |
| 3 |
Hybrid |
T1 |
6.082667 |
0.3528211 |
196 |
5.386854 |
6.778480 |
a |
| 6 |
Creole |
T2 |
4.990000 |
0.3528211 |
196 |
4.294187 |
5.685813 |
a |
| 5 |
Hybrid |
T2 |
4.738667 |
0.3528211 |
196 |
4.042854 |
5.434480 |
a |
| 7 |
Hybrid |
T3 |
5.388000 |
0.3528211 |
196 |
4.692187 |
6.083813 |
a |
| 8 |
Creole |
T3 |
4.208000 |
0.3528211 |
196 |
3.512187 |
4.903813 |
b |
| 10 |
Creole |
T4 |
5.346000 |
0.3528211 |
196 |
4.650187 |
6.041813 |
a |
| 9 |
Hybrid |
T4 |
3.858667 |
0.3528211 |
196 |
3.162854 |
4.554480 |
b |
| 11 |
Hybrid |
T5 |
5.245333 |
0.3528211 |
196 |
4.549520 |
5.941146 |
a |
| 12 |
Creole |
T5 |
4.418000 |
0.3528211 |
196 |
3.722187 |
5.113813 |
a |
| 13 |
Hybrid |
T6 |
5.728667 |
0.3528211 |
196 |
5.032854 |
6.424480 |
a |
| 14 |
Creole |
T6 |
5.324667 |
0.3528211 |
196 |
4.628854 |
6.020480 |
a |
peso_seco_raiz
Diagnostico
Outliers
| 124 |
124 |
T1 |
Hybrid |
2.60 |
1.318667 |
4.904936 |
0.0000009 |
0.0000009345758 |
0.0001953 |
OUTLIER |
| 153 |
153 |
T3 |
Hybrid |
2.88 |
1.516000 |
5.638941 |
0.0000000 |
0.0000000171099 |
0.0000036 |
OUTLIER |
| 193 |
193 |
T5 |
Hybrid |
1.94 |
1.170000 |
4.351953 |
0.0000135 |
0.0000134930040 |
0.0028065 |
OUTLIER |
ANOVA
| trat |
6 |
8.89570540896218 |
1.48261756816036 |
14.9702912365665 |
0.0000000000000489299838764944 |
*** |
| variedad |
1 |
0.208061338505747 |
0.208061338505747 |
2.10083766669894 |
0.148841410422638 |
ns |
| trat:variedad |
6 |
0.273239581034483 |
0.0455399301724139 |
0.459825940427679 |
0.837389504298845 |
ns |
| Residuals |
193 |
19.1142033333333 |
0.099037322970639 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 1 |
Hybrid |
T0 |
0.6646667 |
0.0812557 |
193 |
0.5044035 |
0.8249299 |
a |
| 2 |
Creole |
T0 |
0.6346667 |
0.0812557 |
193 |
0.4744035 |
0.7949299 |
a |
| 4 |
Creole |
T1 |
1.2140000 |
0.0812557 |
193 |
1.0537368 |
1.3742632 |
a |
| 3 |
Hybrid |
T1 |
1.1871429 |
0.0841076 |
193 |
1.0212547 |
1.3530310 |
a |
| 5 |
Hybrid |
T2 |
0.8340000 |
0.0812557 |
193 |
0.6737368 |
0.9942632 |
a |
| 6 |
Creole |
T2 |
0.8133333 |
0.0812557 |
193 |
0.6530701 |
0.9735965 |
a |
| 7 |
Hybrid |
T3 |
1.2557143 |
0.0841076 |
193 |
1.0898261 |
1.4216024 |
a |
| 8 |
Creole |
T3 |
1.0566667 |
0.0812557 |
193 |
0.8964035 |
1.2169299 |
a |
| 9 |
Hybrid |
T4 |
0.9240000 |
0.0812557 |
193 |
0.7637368 |
1.0842632 |
a |
| 10 |
Creole |
T4 |
0.8433333 |
0.0812557 |
193 |
0.6830701 |
1.0035965 |
a |
| 11 |
Hybrid |
T5 |
0.6864286 |
0.0841076 |
193 |
0.5205404 |
0.8523167 |
a |
| 12 |
Creole |
T5 |
0.5526667 |
0.0812557 |
193 |
0.3924035 |
0.7129299 |
a |
| 13 |
Hybrid |
T6 |
0.9426667 |
0.0812557 |
193 |
0.7824035 |
1.1029299 |
a |
| 14 |
Creole |
T6 |
0.9320000 |
0.0812557 |
193 |
0.7717368 |
1.0922632 |
a |
alt_planta
Diagnostico
ANOVA
| trat |
6 |
8203.78095238095 |
1367.29682539682 |
50.9551611949127 |
0.000000000000000000000000000000000000184040121052396 |
*** |
| variedad |
1 |
20.7428571428572 |
20.7428571428572 |
0.773025732031944 |
0.380359284820424 |
ns |
| trat:variedad |
6 |
1204.52380952381 |
200.753968253969 |
7.48151434486839 |
0.000000313700914208267 |
*** |
| Residuals |
196 |
5259.33333333333 |
26.8333333333333 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
30.80000 |
1.337493 |
196 |
28.16227 |
33.43773 |
a |
| 1 |
Hybrid |
T0 |
26.13333 |
1.337493 |
196 |
23.49561 |
28.77106 |
b |
| 3 |
Hybrid |
T1 |
46.33333 |
1.337493 |
196 |
43.69561 |
48.97106 |
a |
| 4 |
Creole |
T1 |
40.26667 |
1.337493 |
196 |
37.62894 |
42.90439 |
b |
| 5 |
Hybrid |
T2 |
44.20000 |
1.337493 |
196 |
41.56227 |
46.83773 |
a |
| 6 |
Creole |
T2 |
39.40000 |
1.337493 |
196 |
36.76227 |
42.03773 |
b |
| 8 |
Creole |
T3 |
38.26667 |
1.337493 |
196 |
35.62894 |
40.90439 |
a |
| 7 |
Hybrid |
T3 |
37.06667 |
1.337493 |
196 |
34.42894 |
39.70439 |
a |
| 10 |
Creole |
T4 |
40.53333 |
1.337493 |
196 |
37.89561 |
43.17106 |
a |
| 9 |
Hybrid |
T4 |
33.06667 |
1.337493 |
196 |
30.42894 |
35.70439 |
b |
| 11 |
Hybrid |
T5 |
30.26667 |
1.337493 |
196 |
27.62894 |
32.90439 |
a |
| 12 |
Creole |
T5 |
27.53333 |
1.337493 |
196 |
24.89561 |
30.17106 |
a |
| 13 |
Hybrid |
T6 |
28.80000 |
1.337493 |
196 |
26.16227 |
31.43773 |
a |
| 14 |
Creole |
T6 |
24.66667 |
1.337493 |
196 |
22.02894 |
27.30439 |
b |
gsr_tallo
Diagnostico
Outliers
| 3 |
3 |
T0 |
Creole |
6.59 |
2.118667 |
4.170295 |
0.0000304 |
0.00003042056 |
0.0063579 |
OUTLIER |
| 139 |
139 |
T2 |
Hybrid |
3.04 |
-2.168000 |
-4.267401 |
0.0000198 |
0.00001977638 |
0.0041530 |
OUTLIER |
ANOVA
| trat |
6 |
36.8710544473915 |
6.14517574123192 |
16.4230125647216 |
0.00000000000000285227440100774 |
*** |
| variedad |
1 |
1.40401584033855 |
1.40401584033855 |
3.7522392780789 |
0.0541891483916545 |
ns |
| trat:variedad |
6 |
5.63568810054818 |
0.939281350091364 |
2.51023405414711 |
0.0231302956641592 |
* |
| Residuals |
194 |
72.5910723809524 |
0.374180785468827 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
4.320000 |
0.1634846 |
194 |
3.997565 |
4.642435 |
a |
| 1 |
Hybrid |
T0 |
3.890667 |
0.1579411 |
194 |
3.579165 |
4.202169 |
a |
| 3 |
Hybrid |
T1 |
4.917333 |
0.1579411 |
194 |
4.605831 |
5.228835 |
a |
| 4 |
Creole |
T1 |
4.849333 |
0.1579411 |
194 |
4.537831 |
5.160835 |
a |
| 5 |
Hybrid |
T2 |
5.362857 |
0.1634846 |
194 |
5.040422 |
5.685292 |
a |
| 6 |
Creole |
T2 |
5.032000 |
0.1579411 |
194 |
4.720498 |
5.343502 |
a |
| 8 |
Creole |
T3 |
4.509333 |
0.1579411 |
194 |
4.197831 |
4.820835 |
a |
| 7 |
Hybrid |
T3 |
4.016000 |
0.1579411 |
194 |
3.704498 |
4.327502 |
b |
| 10 |
Creole |
T4 |
4.316667 |
0.1579411 |
194 |
4.005165 |
4.628169 |
a |
| 9 |
Hybrid |
T4 |
3.704000 |
0.1579411 |
194 |
3.392498 |
4.015502 |
b |
| 12 |
Creole |
T5 |
4.195333 |
0.1579411 |
194 |
3.883831 |
4.506835 |
a |
| 11 |
Hybrid |
T5 |
4.066667 |
0.1579411 |
194 |
3.755165 |
4.378169 |
a |
| 13 |
Hybrid |
T6 |
4.218000 |
0.1579411 |
194 |
3.906498 |
4.529502 |
a |
| 14 |
Creole |
T6 |
4.095333 |
0.1579411 |
194 |
3.783831 |
4.406835 |
a |
nhp_hoja
Diagnostico
Outliers
| 4 |
4 |
T0 |
Creole |
6 |
1.0000000 |
10.117361 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
| 5 |
5 |
T0 |
Creole |
4 |
-1.0000000 |
-10.117361 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
| 16 |
16 |
T1 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 17 |
17 |
T1 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 18 |
18 |
T1 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 19 |
19 |
T1 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 20 |
20 |
T1 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 21 |
21 |
T1 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 22 |
22 |
T1 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 23 |
23 |
T1 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 24 |
24 |
T1 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 25 |
25 |
T1 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 26 |
26 |
T1 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 27 |
27 |
T1 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 28 |
28 |
T1 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 29 |
29 |
T1 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 30 |
30 |
T1 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 31 |
31 |
T2 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 32 |
32 |
T2 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 33 |
33 |
T2 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 34 |
34 |
T2 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 35 |
35 |
T2 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 36 |
36 |
T2 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 37 |
37 |
T2 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 38 |
38 |
T2 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 39 |
39 |
T2 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 40 |
40 |
T2 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 41 |
41 |
T2 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 42 |
42 |
T2 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 43 |
43 |
T2 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 44 |
44 |
T2 |
Creole |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 45 |
45 |
T2 |
Creole |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 51 |
51 |
T3 |
Creole |
6 |
0.8666667 |
8.768380 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
| 57 |
57 |
T3 |
Creole |
6 |
0.8666667 |
8.768380 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
| 62 |
62 |
T4 |
Creole |
6 |
0.7333333 |
7.419398 |
0.0000000 |
0.0000000000001176836 |
0.0000000 |
OUTLIER |
| 65 |
65 |
T4 |
Creole |
6 |
0.7333333 |
7.419398 |
0.0000000 |
0.0000000000001176836 |
0.0000000 |
OUTLIER |
| 66 |
66 |
T4 |
Creole |
6 |
0.7333333 |
7.419398 |
0.0000000 |
0.0000000000001176836 |
0.0000000 |
OUTLIER |
| 72 |
72 |
T4 |
Creole |
6 |
0.7333333 |
7.419398 |
0.0000000 |
0.0000000000001176836 |
0.0000000 |
OUTLIER |
| 88 |
88 |
T5 |
Creole |
6 |
0.9333333 |
9.442871 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
| 112 |
112 |
T0 |
Hybrid |
4 |
-0.6666667 |
-6.744908 |
0.0000000 |
0.0000000000153124180 |
0.0000000 |
OUTLIER |
| 114 |
114 |
T0 |
Hybrid |
4 |
-0.6666667 |
-6.744908 |
0.0000000 |
0.0000000000153124180 |
0.0000000 |
OUTLIER |
| 116 |
116 |
T0 |
Hybrid |
4 |
-0.6666667 |
-6.744908 |
0.0000000 |
0.0000000000153124180 |
0.0000000 |
OUTLIER |
| 119 |
119 |
T0 |
Hybrid |
4 |
-0.6666667 |
-6.744908 |
0.0000000 |
0.0000000000153124180 |
0.0000000 |
OUTLIER |
| 120 |
120 |
T0 |
Hybrid |
4 |
-0.6666667 |
-6.744908 |
0.0000000 |
0.0000000000153124180 |
0.0000000 |
OUTLIER |
| 121 |
121 |
T1 |
Hybrid |
6 |
0.5333333 |
5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 122 |
122 |
T1 |
Hybrid |
6 |
0.5333333 |
5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 123 |
123 |
T1 |
Hybrid |
5 |
-0.4666667 |
-4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 124 |
124 |
T1 |
Hybrid |
6 |
0.5333333 |
5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 125 |
125 |
T1 |
Hybrid |
6 |
0.5333333 |
5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 126 |
126 |
T1 |
Hybrid |
6 |
0.5333333 |
5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 127 |
127 |
T1 |
Hybrid |
6 |
0.5333333 |
5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 128 |
128 |
T1 |
Hybrid |
5 |
-0.4666667 |
-4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 129 |
129 |
T1 |
Hybrid |
5 |
-0.4666667 |
-4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 130 |
130 |
T1 |
Hybrid |
5 |
-0.4666667 |
-4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 131 |
131 |
T1 |
Hybrid |
5 |
-0.4666667 |
-4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 132 |
132 |
T1 |
Hybrid |
6 |
0.5333333 |
5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 133 |
133 |
T1 |
Hybrid |
5 |
-0.4666667 |
-4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 134 |
134 |
T1 |
Hybrid |
5 |
-0.4666667 |
-4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 135 |
135 |
T1 |
Hybrid |
5 |
-0.4666667 |
-4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 136 |
136 |
T2 |
Hybrid |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 137 |
137 |
T2 |
Hybrid |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 138 |
138 |
T2 |
Hybrid |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 139 |
139 |
T2 |
Hybrid |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 140 |
140 |
T2 |
Hybrid |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 141 |
141 |
T2 |
Hybrid |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 142 |
142 |
T2 |
Hybrid |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 143 |
143 |
T2 |
Hybrid |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 144 |
144 |
T2 |
Hybrid |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 145 |
145 |
T2 |
Hybrid |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 146 |
146 |
T2 |
Hybrid |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 147 |
147 |
T2 |
Hybrid |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 148 |
148 |
T2 |
Hybrid |
6 |
0.4666667 |
4.721435 |
0.0000023 |
0.0000023418611010406 |
0.0003817 |
OUTLIER |
| 149 |
149 |
T2 |
Hybrid |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 150 |
150 |
T2 |
Hybrid |
5 |
-0.5333333 |
-5.395926 |
0.0000001 |
0.0000000681710119466 |
0.0000130 |
OUTLIER |
| 152 |
152 |
T3 |
Hybrid |
6 |
0.8666667 |
8.768380 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
| 155 |
155 |
T3 |
Hybrid |
6 |
0.8666667 |
8.768380 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
| 166 |
166 |
T4 |
Hybrid |
6 |
0.8666667 |
8.768380 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
| 173 |
173 |
T4 |
Hybrid |
6 |
0.8666667 |
8.768380 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
| 181 |
181 |
T5 |
Hybrid |
6 |
0.9333333 |
9.442871 |
0.0000000 |
0.0000000000000000000 |
0.0000000 |
OUTLIER |
ANOVA
| trat |
4 |
0.000000000000000000000000000448210187366446 |
0.000000000000000000000000000112052546841611 |
1.17465980750084 |
0.325411307782507 |
ns |
| variedad |
1 |
0.0000000000000000000000000000724842873482364 |
0.0000000000000000000000000000724842873482364 |
0.759861167133205 |
0.385098313680927 |
ns |
| trat:variedad |
4 |
0.000000000000000000000000000345717525650355 |
0.0000000000000000000000000000864293814125887 |
0.906049200970291 |
0.46279233587861 |
ns |
| Residuals |
121 |
0.0000000000000000000000000115423700387614 |
0.0000000000000000000000000000953914879236483 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
5 |
0 |
121 |
5 |
5 |
a |
| 1 |
Hybrid |
T0 |
5 |
0 |
121 |
5 |
5 |
b |
| 3 |
Hybrid |
T3 |
5 |
0 |
121 |
5 |
5 |
a |
| 4 |
Creole |
T3 |
5 |
0 |
121 |
5 |
5 |
a |
| 5 |
Hybrid |
T4 |
5 |
0 |
121 |
5 |
5 |
a |
| 6 |
Creole |
T4 |
5 |
0 |
121 |
5 |
5 |
a |
| 7 |
Hybrid |
T5 |
5 |
0 |
121 |
5 |
5 |
a |
| 8 |
Creole |
T5 |
5 |
0 |
121 |
5 |
5 |
a |
| 9 |
Hybrid |
T6 |
5 |
0 |
121 |
5 |
5 |
a |
| 10 |
Creole |
T6 |
5 |
0 |
121 |
5 |
5 |
a |
larg_hoja
Diagnostico
ANOVA
| trat |
6 |
3517.25714285714 |
586.209523809524 |
44.1413789570741 |
0.000000000000000000000000000000000679814077816785 |
*** |
| variedad |
1 |
12.8761904761905 |
12.8761904761905 |
0.96957278967319 |
0.326000686912924 |
ns |
| trat:variedad |
6 |
830.857142857144 |
138.476190476191 |
10.4272103268108 |
0.000000000500170136061446 |
*** |
| Residuals |
196 |
2602.93333333333 |
13.2802721088435 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
26.20000 |
0.9409312 |
196 |
24.34435 |
28.05565 |
a |
| 1 |
Hybrid |
T0 |
22.66667 |
0.9409312 |
196 |
20.81102 |
24.52232 |
b |
| 3 |
Hybrid |
T1 |
36.26667 |
0.9409312 |
196 |
34.41102 |
38.12232 |
a |
| 4 |
Creole |
T1 |
28.33333 |
0.9409312 |
196 |
26.47768 |
30.18898 |
b |
| 6 |
Creole |
T2 |
34.80000 |
0.9409312 |
196 |
32.94435 |
36.65565 |
a |
| 5 |
Hybrid |
T2 |
33.86667 |
0.9409312 |
196 |
32.01102 |
35.72232 |
a |
| 7 |
Hybrid |
T3 |
28.93333 |
0.9409312 |
196 |
27.07768 |
30.78898 |
a |
| 8 |
Creole |
T3 |
28.60000 |
0.9409312 |
196 |
26.74435 |
30.45565 |
a |
| 10 |
Creole |
T4 |
28.26667 |
0.9409312 |
196 |
26.41102 |
30.12232 |
a |
| 9 |
Hybrid |
T4 |
23.33333 |
0.9409312 |
196 |
21.47768 |
25.18898 |
b |
| 11 |
Hybrid |
T5 |
26.46667 |
0.9409312 |
196 |
24.61102 |
28.32232 |
a |
| 12 |
Creole |
T5 |
23.40000 |
0.9409312 |
196 |
21.54435 |
25.25565 |
b |
| 13 |
Hybrid |
T6 |
23.06667 |
0.9409312 |
196 |
21.21102 |
24.92232 |
a |
| 14 |
Creole |
T6 |
21.53333 |
0.9409312 |
196 |
19.67768 |
23.38898 |
a |
grs_hoja
Diagnostico
Outliers
| 193 |
193 |
T5 |
Hybrid |
1.43 |
0.5793333 |
3.757259 |
0.0001718 |
0.0001717844 |
0.0359029 |
OUTLIER |
| 197 |
197 |
T6 |
Hybrid |
1.45 |
0.6780000 |
4.397161 |
0.0000110 |
0.0000109676 |
0.0023032 |
OUTLIER |
ANOVA
| trat |
6 |
1.87838186229001 |
0.313063643715002 |
10.961029990429 |
0.000000000166359834805947 |
*** |
| variedad |
1 |
0.000634076170869784 |
0.000634076170869784 |
0.022200367448119 |
0.881710078225411 |
ns |
| trat:variedad |
6 |
0.632610833517145 |
0.105435138919524 |
3.69151047380647 |
0.00169800034430951 |
** |
| Residuals |
194 |
5.54093428571428 |
0.0285615169366716 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
0.9700000 |
0.0436360 |
194 |
0.8839381 |
1.0560619 |
a |
| 1 |
Hybrid |
T0 |
0.8846667 |
0.0436360 |
194 |
0.7986048 |
0.9707285 |
a |
| 3 |
Hybrid |
T1 |
0.8446667 |
0.0436360 |
194 |
0.7586048 |
0.9307285 |
a |
| 4 |
Creole |
T1 |
0.6346667 |
0.0436360 |
194 |
0.5486048 |
0.7207285 |
b |
| 5 |
Hybrid |
T2 |
0.7553333 |
0.0436360 |
194 |
0.6692715 |
0.8413952 |
a |
| 6 |
Creole |
T2 |
0.7006667 |
0.0436360 |
194 |
0.6146048 |
0.7867285 |
a |
| 7 |
Hybrid |
T3 |
0.6880000 |
0.0436360 |
194 |
0.6019381 |
0.7740619 |
a |
| 8 |
Creole |
T3 |
0.6300000 |
0.0436360 |
194 |
0.5439381 |
0.7160619 |
a |
| 10 |
Creole |
T4 |
0.6853333 |
0.0436360 |
194 |
0.5992715 |
0.7713952 |
a |
| 9 |
Hybrid |
T4 |
0.6366667 |
0.0436360 |
194 |
0.5506048 |
0.7227285 |
a |
| 12 |
Creole |
T5 |
0.9406667 |
0.0436360 |
194 |
0.8546048 |
1.0267285 |
a |
| 11 |
Hybrid |
T5 |
0.8092857 |
0.0451676 |
194 |
0.7202032 |
0.8983682 |
b |
| 14 |
Creole |
T6 |
0.8126667 |
0.0436360 |
194 |
0.7266048 |
0.8987285 |
a |
| 13 |
Hybrid |
T6 |
0.7235714 |
0.0451676 |
194 |
0.6344889 |
0.8126539 |
a |
anch_hoja
Diagnostico
Outliers
| 141 |
141 |
T2 |
Hybrid |
30.38 |
8.610667 |
3.680491 |
0.0002328 |
0.0002327851 |
0.0488849 |
OUTLIER |
ANOVA
| trat |
6 |
251.760531761535 |
41.9600886269226 |
6.40463281705183 |
0.00000355932128610583 |
*** |
| variedad |
1 |
0.0145229907818354 |
0.0145229907818354 |
0.00221673562680236 |
0.962495880334166 |
ns |
| trat:variedad |
6 |
116.659083347477 |
19.4431805579129 |
2.96773520609711 |
0.00853268555104408 |
** |
| Residuals |
195 |
1277.54666285714 |
6.55152134798535 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
18.86800 |
0.6608843 |
195 |
17.56460 |
20.17140 |
a |
| 1 |
Hybrid |
T0 |
16.61133 |
0.6608843 |
195 |
15.30793 |
17.91473 |
b |
| 3 |
Hybrid |
T1 |
20.04333 |
0.6608843 |
195 |
18.73993 |
21.34673 |
a |
| 4 |
Creole |
T1 |
18.95867 |
0.6608843 |
195 |
17.65527 |
20.26207 |
a |
| 5 |
Hybrid |
T2 |
21.15429 |
0.6840803 |
195 |
19.80514 |
22.50343 |
a |
| 6 |
Creole |
T2 |
19.03467 |
0.6608843 |
195 |
17.73127 |
20.33807 |
b |
| 8 |
Creole |
T3 |
19.05067 |
0.6608843 |
195 |
17.74727 |
20.35407 |
a |
| 7 |
Hybrid |
T3 |
17.51800 |
0.6608843 |
195 |
16.21460 |
18.82140 |
a |
| 10 |
Creole |
T4 |
17.06933 |
0.6608843 |
195 |
15.76593 |
18.37273 |
a |
| 9 |
Hybrid |
T4 |
16.07400 |
0.6608843 |
195 |
14.77060 |
17.37740 |
a |
| 11 |
Hybrid |
T5 |
19.51800 |
0.6608843 |
195 |
18.21460 |
20.82140 |
a |
| 12 |
Creole |
T5 |
18.28667 |
0.6608843 |
195 |
16.98327 |
19.59007 |
a |
| 13 |
Hybrid |
T6 |
17.86067 |
0.6608843 |
195 |
16.55727 |
19.16407 |
a |
| 14 |
Creole |
T6 |
17.55467 |
0.6608843 |
195 |
16.25127 |
18.85807 |
a |
peso_fres_brote
Diagnostico
ANOVA
| trat |
6 |
225.698459161905 |
37.6164098603175 |
28.7634400302011 |
0.00000000000000000000000146687997518286 |
*** |
| variedad |
1 |
0.375666304761905 |
0.375666304761905 |
0.287253761550096 |
0.59259348215828 |
ns |
| trat:variedad |
6 |
67.1975631619048 |
11.1995938603175 |
8.56378499596482 |
0.0000000284330749762502 |
*** |
| Residuals |
196 |
256.3259584 |
1.30778550204082 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
5.392000 |
0.2952722 |
196 |
4.809681 |
5.974319 |
a |
| 1 |
Hybrid |
T0 |
3.613333 |
0.2952722 |
196 |
3.031015 |
4.195652 |
b |
| 3 |
Hybrid |
T1 |
7.640000 |
0.2952722 |
196 |
7.057681 |
8.222319 |
a |
| 4 |
Creole |
T1 |
5.770000 |
0.2952722 |
196 |
5.187681 |
6.352319 |
b |
| 5 |
Hybrid |
T2 |
7.163333 |
0.2952722 |
196 |
6.581015 |
7.745652 |
a |
| 6 |
Creole |
T2 |
6.562000 |
0.2952722 |
196 |
5.979681 |
7.144319 |
a |
| 8 |
Creole |
T3 |
5.699333 |
0.2952722 |
196 |
5.117015 |
6.281652 |
a |
| 7 |
Hybrid |
T3 |
4.771333 |
0.2952722 |
196 |
4.189015 |
5.353652 |
b |
| 10 |
Creole |
T4 |
5.014000 |
0.2952722 |
196 |
4.431681 |
5.596319 |
a |
| 9 |
Hybrid |
T4 |
4.094000 |
0.2952722 |
196 |
3.511681 |
4.676319 |
b |
| 11 |
Hybrid |
T5 |
4.720000 |
0.2952722 |
196 |
4.137681 |
5.302319 |
a |
| 12 |
Creole |
T5 |
4.684800 |
0.2952722 |
196 |
4.102481 |
5.267119 |
a |
| 13 |
Hybrid |
T6 |
4.303333 |
0.2952722 |
196 |
3.721015 |
4.885652 |
a |
| 14 |
Creole |
T6 |
3.775333 |
0.2952722 |
196 |
3.193015 |
4.357652 |
a |
peso_seco_brote
Diagnostico
Outliers
| 42 |
42 |
T2 |
Creole |
2.39 |
1.208667 |
3.733899 |
0.0001885 |
0.00018853852782 |
0.0384619 |
OUTLIER |
| 72 |
72 |
T4 |
Creole |
3.09 |
1.768000 |
5.461830 |
0.0000000 |
0.00000004712499 |
0.0000099 |
OUTLIER |
| 127 |
127 |
T1 |
Hybrid |
3.27 |
1.246667 |
3.851291 |
0.0001175 |
0.00011749690142 |
0.0240869 |
OUTLIER |
| 134 |
134 |
T1 |
Hybrid |
0.73 |
-1.293333 |
-3.995457 |
0.0000646 |
0.00006456965482 |
0.0133013 |
OUTLIER |
| 167 |
167 |
T4 |
Hybrid |
0.27 |
-1.193333 |
-3.686530 |
0.0002273 |
0.00022733296863 |
0.0461486 |
OUTLIER |
| 169 |
169 |
T4 |
Hybrid |
2.85 |
1.386667 |
4.283789 |
0.0000184 |
0.00001837374658 |
0.0038034 |
OUTLIER |
| 170 |
170 |
T4 |
Hybrid |
2.93 |
1.466667 |
4.530930 |
0.0000059 |
0.00000587245145 |
0.0012215 |
OUTLIER |
| 172 |
172 |
T4 |
Hybrid |
2.98 |
1.516667 |
4.685394 |
0.0000028 |
0.00000279422122 |
0.0005840 |
OUTLIER |
ANOVA
| trat |
6 |
43.1935007107252 |
7.19891678512086 |
42.4711267015912 |
0.000000000000000000000000000000016240034181697 |
*** |
| variedad |
1 |
0.0152306451812492 |
0.0152306451812492 |
0.0898555547380109 |
0.764692030144416 |
ns |
| trat:variedad |
6 |
3.37242174716203 |
0.562070291193672 |
3.31602090495423 |
0.00398028439930072 |
** |
| Residuals |
188 |
31.8662691741592 |
0.169501431777442 |
|
|
|
| — |
|
|
|
|
|
|
| Significance: |
|
0.001 *** |
0.01 ** |
0.05 * |
|
|
Mean comparison
| 2 |
Creole |
T0 |
0.7100000 |
0.1063019 |
188 |
0.5003022 |
0.9196978 |
a |
| 1 |
Hybrid |
T0 |
0.4253333 |
0.1063019 |
188 |
0.2156355 |
0.6350311 |
a |
| 3 |
Hybrid |
T1 |
2.0269231 |
0.1141866 |
188 |
1.8016715 |
2.2521747 |
a |
| 4 |
Creole |
T1 |
1.5046667 |
0.1063019 |
188 |
1.2949689 |
1.7143645 |
b |
| 5 |
Hybrid |
T2 |
1.2113333 |
0.1063019 |
188 |
1.0016355 |
1.4210311 |
a |
| 6 |
Creole |
T2 |
1.0950000 |
0.1100329 |
188 |
0.8779421 |
1.3120579 |
a |
| 8 |
Creole |
T3 |
1.7186667 |
0.1063019 |
188 |
1.5089689 |
1.9283645 |
a |
| 7 |
Hybrid |
T3 |
1.4093333 |
0.1063019 |
188 |
1.1996355 |
1.6190311 |
b |
| 10 |
Creole |
T4 |
1.1957143 |
0.1100329 |
188 |
0.9786564 |
1.4127721 |
a |
| 9 |
Hybrid |
T4 |
1.1745455 |
0.1241339 |
188 |
0.9296712 |
1.4194197 |
a |
| 12 |
Creole |
T5 |
0.6500000 |
0.1063019 |
188 |
0.4403022 |
0.8596978 |
a |
| 11 |
Hybrid |
T5 |
0.6373333 |
0.1063019 |
188 |
0.4276355 |
0.8470311 |
a |
| 14 |
Creole |
T6 |
0.5486667 |
0.1063019 |
188 |
0.3389689 |
0.7583645 |
a |
| 13 |
Hybrid |
T6 |
0.4586667 |
0.1063019 |
188 |
0.2489689 |
0.6683645 |
a |
PCA
blues <- 1:length(rsl) %>% map(\(x) {
rsl[[x]]$mc %>%
select(1:3)
}) %>%
Reduce(function(...) merge(..., all = TRUE), .)
blues %>% str()
## 'data.frame': 14 obs. of 15 variables:
## $ variedad : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 2 2 2 ...
## $ trat : Factor w/ 7 levels "T0","T1","T2",..: 1 2 3 4 5 6 7 1 2 3 ...
## $ raiz_lgtd : num 11 14 13.3 12.9 15.1 ...
## $ gsr_raiz : num 1.256 0.749 0.926 0.86 0.985 ...
## $ num_raiz : num 10.9 14.5 15.1 10.7 13.4 ...
## $ peso_fres_raiz : num 4.7 6.62 4.99 4.21 5.35 ...
## $ peso_seco_raiz : num 0.635 1.214 0.813 1.057 0.843 ...
## $ alt_planta : num 30.8 40.3 39.4 38.3 40.5 ...
## $ gsr_tallo : num 4.32 4.85 5.03 4.51 4.32 ...
## $ nhp_hoja : num 5 NA NA 5 5 ...
## $ larg_hoja : num 26.2 28.3 34.8 28.6 28.3 ...
## $ grs_hoja : num 0.97 0.635 0.701 0.63 0.685 ...
## $ anch_hoja : num 18.9 19 19 19.1 17.1 ...
## $ peso_fres_brote: num 5.39 5.77 6.56 5.7 5.01 ...
## $ peso_seco_brote: num 0.71 1.5 1.09 1.72 1.2 ...
pca <- blues %>%
select(!c(nhp_hoja)) %>%
unite("treat", c(trat, variedad), remove = F, sep = "-") %>%
column_to_rownames("treat") %>%
PCA(scale.unit = T, quali.sup = c(1:2), graph = F)
summary(pca, nbelements = Inf, nb.dec = 2)
##
## Call:
## PCA(X = ., scale.unit = T, quali.sup = c(1:2), graph = F)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7 Dim.8
## Variance 6.27 2.46 1.22 0.79 0.55 0.32 0.20 0.09
## % of var. 52.22 20.54 10.20 6.57 4.59 2.65 1.64 0.72
## Cumulative % of var. 52.22 72.76 82.96 89.54 94.12 96.77 98.41 99.13
## Dim.9 Dim.10 Dim.11 Dim.12
## Variance 0.06 0.03 0.01 0.00
## % of var. 0.53 0.23 0.11 0.01
## Cumulative % of var. 99.66 99.89 99.99 100.00
##
## Individuals
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## T0-Creole | 4.15 | -1.64 3.07 0.16 | 2.99 25.86 0.52 | -1.37 10.97
## T1-Creole | 3.89 | 3.03 10.44 0.61 | -1.65 7.88 0.18 | 1.40 11.38
## T2-Creole | 3.15 | 2.46 6.89 0.61 | 1.00 2.88 0.10 | 0.02 0.00
## T3-Creole | 2.86 | 1.26 1.82 0.20 | -0.85 2.08 0.09 | -2.16 27.31
## T4-Creole | 1.99 | 0.26 0.08 0.02 | -0.77 1.73 0.15 | 0.82 3.89
## T5-Creole | 3.56 | -2.85 9.28 0.64 | 1.59 7.31 0.20 | 0.67 2.58
## T6-Creole | 3.08 | -2.74 8.55 0.79 | -0.43 0.53 0.02 | 0.81 3.80
## T0-Hybrid | 4.00 | -3.75 16.03 0.88 | 0.17 0.08 0.00 | 0.92 4.95
## T1-Hybrid | 5.13 | 4.70 25.13 0.84 | 0.81 1.90 0.02 | 1.15 7.69
## T2-Hybrid | 3.97 | 2.81 8.98 0.50 | 2.13 13.09 0.29 | -0.76 3.40
## T3-Hybrid | 2.92 | 1.07 1.30 0.13 | -2.18 13.77 0.56 | -0.73 3.10
## T4-Hybrid | 3.68 | -1.68 3.22 0.21 | -2.59 19.46 0.50 | -1.64 15.76
## T5-Hybrid | 2.02 | -1.06 1.28 0.27 | 0.66 1.26 0.11 | -0.05 0.01
## T6-Hybrid | 2.69 | -1.85 3.92 0.47 | -0.86 2.17 0.10 | 0.94 5.16
## cos2
## T0-Creole 0.11 |
## T1-Creole 0.13 |
## T2-Creole 0.00 |
## T3-Creole 0.57 |
## T4-Creole 0.17 |
## T5-Creole 0.03 |
## T6-Creole 0.07 |
## T0-Hybrid 0.05 |
## T1-Hybrid 0.05 |
## T2-Hybrid 0.04 |
## T3-Hybrid 0.06 |
## T4-Hybrid 0.20 |
## T5-Hybrid 0.00 |
## T6-Hybrid 0.12 |
##
## Variables
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## raiz_lgtd | -0.37 2.21 0.14 | -0.20 1.57 0.04 | 0.66 36.02 0.44 |
## gsr_raiz | -0.56 5.07 0.32 | 0.75 22.69 0.56 | 0.09 0.60 0.01 |
## num_raiz | 0.78 9.77 0.61 | 0.02 0.02 0.00 | 0.42 14.44 0.18 |
## peso_fres_raiz | 0.48 3.61 0.23 | -0.15 0.90 0.02 | 0.70 39.99 0.49 |
## peso_seco_raiz | 0.64 6.50 0.41 | -0.64 16.61 0.41 | 0.04 0.12 0.00 |
## alt_planta | 0.95 14.40 0.90 | -0.01 0.00 0.00 | -0.08 0.58 0.01 |
## gsr_tallo | 0.81 10.40 0.65 | 0.43 7.56 0.19 | 0.10 0.84 0.01 |
## larg_hoja | 0.92 13.57 0.85 | 0.27 2.99 0.07 | -0.05 0.23 0.00 |
## grs_hoja | -0.43 2.96 0.19 | 0.76 23.41 0.58 | 0.21 3.53 0.04 |
## anch_hoja | 0.65 6.77 0.42 | 0.61 14.87 0.37 | -0.05 0.21 0.00 |
## peso_fres_brote | 0.90 12.88 0.81 | 0.41 6.82 0.17 | -0.06 0.25 0.00 |
## peso_seco_brote | 0.86 11.86 0.74 | -0.25 2.56 0.06 | -0.20 3.20 0.04 |
##
## Supplementary categories
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test Dim.3 cos2
## Creole | 0.40 | -0.03 0.01 -0.05 | 0.27 0.45 0.62 | 0.02 0.00
## Hybrid | 0.40 | 0.03 0.01 0.05 | -0.27 0.45 -0.62 | -0.02 0.00
## T0 | 3.24 | -2.70 0.69 -1.59 | 1.58 0.24 1.48 | -0.23 0.00
## T1 | 4.15 | 3.86 0.87 2.27 | -0.42 0.01 -0.39 | 1.27 0.09
## T2 | 3.31 | 2.63 0.63 1.55 | 1.56 0.22 1.46 | -0.37 0.01
## T3 | 2.46 | 1.16 0.22 0.68 | -1.51 0.38 -1.42 | -1.45 0.34
## T4 | 2.24 | -0.71 0.10 -0.42 | -1.68 0.57 -1.58 | -0.41 0.03
## T5 | 2.43 | -1.96 0.65 -1.15 | 1.12 0.21 1.05 | 0.31 0.02
## T6 | 2.79 | -2.30 0.68 -1.35 | -0.65 0.05 -0.61 | 0.87 0.10
## v.test
## Creole 0.08 |
## Hybrid -0.08 |
## T0 -0.30 |
## T1 1.69 |
## T2 -0.49 |
## T3 -1.92 |
## T4 -0.55 |
## T5 0.41 |
## T6 1.16 |